Abstract
Classified e-commerce sites have seen a rapid growth in the last few years with the availability of internet to mass people. But as most sites do not offer an intelligent recommender system, ordinary customers looking for a specific product face the daunting task of finding the perfect product in accordance with his requirements and budget. Besides, most of the time, sellers do not have the idea about the exact market value of their item. It results in either under valuation or over valuation which hampers to get a good price that could benefit both the seller and the buyer. We propose a fuzzy logic based intelligent recommender system which will intelligently recommend products most suitable with buyer’s requirements. It does not require extensive user information. Though we have used it only for mobile devices in the experiment, results indicate that the system is effective and efficient, and can be implemented for any product based on their features.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Schafer, J.B., Konstan, J., Riedi, J.: Electronic commerce recommender applications. J. Data Min. Knowl. Disc. 5(1–2), 115–152 (2001)
Resnick, P., Varian, H.: Recommender systems. Commun. ACM 40(3), 56–58 (1997)
Chen, D.N., Paul, J.H.H., Kuo, Y.R., Liang, T.P.: A web based personalized recommendation system for mobile phone selection: design, implementation, and evaluation. J. Expert Syst. Appl. 37(12), 8201–8210 (2010)
Weng, S.S., Liu, M.-J.: Personalized product recommendation in e-commerce. In: Proceedings of the 2004 IEEE International Conference on e-Technology, e-Commerce and e-Service, pp. 413–420 (2004)
Lee, W.P., Liu, C.H.: Intelligent agent-based systems for personalized recommendations in internet commerce. Expert Syst. Appl. 22(4), 275–284 (2002)
Huang, L., Dai, L., Wei, Y., Huang, M.: A personalized recommendation system based on multi-agent. In: Second International Conference on Genetic and Evolutionary Computing, WGEC 2008 (2008)
Cho, H., Jeong, O.-R., Lee, E.: A personalized recommendation system based on product-specific weights and improved user behavior analysis. In: Proceedings of the 4th International Conference on Uniquitous Information Management and Communication, ICUIMC, Article No. 57 (2010)
Debnath, S., Ganguly, N., Mitra, P.: Feature weighting in content based recommendation system using social network analysis. In: Proceedings of the 17th International Conference on World Wide Web, pp. 1041–1042 (2008)
Zahiduzzaman, A.K.M., Quasem, M.N., Ahmed, F., Rahman, R.M.: Indexing Bangla newspaper articles using fuzzy and crisp clustering algorithms. In: Proceedings of 13th International Conference on Enterprise Information Systems (ICEIS 2011), vol. 1, pp. 361–364 (2011)
Mendel, J.: Fuzzy logic systems for engineering. A tutorial. Proc. IEEE 83(3), 345–377 (1995)
Tsai, H.C., Hsiao, S.-W.: Evaluation of alternatives for product customization using fuzzy logic. J. Inf. Sci. 158, 233–262 (2004)
Schafer, J.B., Konstan, J., Riedi, J.: Recommender systems in e-commerce. In: Proceedings of the First ACM Conference on Electronic Commerce (1999)
Mobashe, B., Dai, H., Luo, T.: Discovery and evaluation of aggregate usage profiles for web personalization. Data Min. Knowl. Disc. 6(1), 61–82 (2002)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Sharif, U., Kamal, M.R., Rahman, R.M. (2017). A Fuzzy Logic Based Recommendation System for Classified Advertisement Websites. In: Silhavy, R., Senkerik, R., Kominkova Oplatkova, Z., Prokopova, Z., Silhavy, P. (eds) Artificial Intelligence Trends in Intelligent Systems. CSOC 2017. Advances in Intelligent Systems and Computing, vol 573. Springer, Cham. https://doi.org/10.1007/978-3-319-57261-1_25
Download citation
DOI: https://doi.org/10.1007/978-3-319-57261-1_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-57260-4
Online ISBN: 978-3-319-57261-1
eBook Packages: EngineeringEngineering (R0)